
"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


a       :						int       		=		logging.get_logger(__name__)

a       :						Optional[Any]       		=		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a       :						int       		=		{
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

a       :						List[str]       		=		{
    '''roberta-base''': 5_1_2,
    '''roberta-large''': 5_1_2,
    '''roberta-large-mnli''': 5_1_2,
    '''distilroberta-base''': 5_1_2,
    '''roberta-base-openai-detector''': 5_1_2,
    '''roberta-large-openai-detector''': 5_1_2,
}




class 		a_    (			_UpperCAmelCase						):
    a   :					Any			     =					VOCAB_FILES_NAMES
    a   :					Dict			     =					PRETRAINED_VOCAB_FILES_MAP
    a   :					Any			     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a   :					int			     =					['input_ids', 'attention_mask']
    a   :					Any			     =					RobertaTokenizer







    def __init__(      self      :							List[str]  ,       __UpperCamelCase      :							str=None  ,       __UpperCamelCase      :							Optional[Any]=None  ,       __UpperCamelCase      :							List[str]=None  ,       __UpperCamelCase      :							Optional[int]="replace"  ,       __UpperCamelCase      :							Tuple="<s>"  ,       __UpperCamelCase      :							Union[str, Any]="</s>"  ,       __UpperCamelCase      :							int="</s>"  ,       __UpperCamelCase      :							Tuple="<s>"  ,       __UpperCamelCase      :							int="<unk>"  ,       __UpperCamelCase      :							Optional[Any]="<pad>"  ,       __UpperCamelCase      :							Optional[int]="<mask>"  ,       __UpperCamelCase      :							str=False  ,       __UpperCamelCase      :							str=True  ,       **__UpperCamelCase      :							int  ,       )		->int:






        '''simple docstring'''






        super().__init__(
            __UpperCamelCase  ,       __UpperCamelCase  ,       tokenizer_file=__UpperCamelCase  ,       errors=__UpperCamelCase  ,       bos_token=__UpperCamelCase  ,       eos_token=__UpperCamelCase  ,       sep_token=__UpperCamelCase  ,       cls_token=__UpperCamelCase  ,       unk_token=__UpperCamelCase  ,       pad_token=__UpperCamelCase  ,       mask_token=__UpperCamelCase  ,       add_prefix_space=__UpperCamelCase  ,       trim_offsets=__UpperCamelCase  ,       **__UpperCamelCase  ,       )

        _UpperCAmelCase        = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
        if pre_tok_state.get("""add_prefix_space"""  ,       __UpperCamelCase  ) != add_prefix_space:
            _UpperCAmelCase        = getattr(__UpperCamelCase  ,       pre_tok_state.pop("""type"""  )  )
            _UpperCAmelCase        = add_prefix_space
            _UpperCAmelCase        = pre_tok_class(**__UpperCamelCase  )

        _UpperCAmelCase        = add_prefix_space

        _UpperCAmelCase        = """post_processor"""
        _UpperCAmelCase        = getattr(self.backend_tokenizer  ,       __UpperCamelCase  ,       __UpperCamelCase  )
        if tokenizer_component_instance:
            _UpperCAmelCase        = json.loads(tokenizer_component_instance.__getstate__()  )

            # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
            if "sep" in state:
                _UpperCAmelCase        = tuple(state["""sep"""]  )
            if "cls" in state:
                _UpperCAmelCase        = tuple(state["""cls"""]  )

            _UpperCAmelCase        = False

            if state.get("""add_prefix_space"""  ,       __UpperCamelCase  ) != add_prefix_space:
                _UpperCAmelCase        = add_prefix_space
                _UpperCAmelCase        = True

            if state.get("""trim_offsets"""  ,       __UpperCamelCase  ) != trim_offsets:
                _UpperCAmelCase        = trim_offsets
                _UpperCAmelCase        = True

            if changes_to_apply:
                _UpperCAmelCase        = getattr(__UpperCamelCase  ,       state.pop("""type"""  )  )
                _UpperCAmelCase        = component_class(**__UpperCamelCase  )
                setattr(self.backend_tokenizer  ,       __UpperCamelCase  ,       __UpperCamelCase  )







    @property
    def   _snake_case       (      self      :							Any  )		->str:






        '''simple docstring'''






        if self._mask_token is None:
            if self.verbose:
                logger.error("""Using mask_token, but it is not set yet."""  )
            return None
        return str(self._mask_token  )







    @mask_token.setter
    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							Dict  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = AddedToken(__UpperCamelCase  ,       lstrip=__UpperCamelCase  ,       rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase  ,       __UpperCamelCase  ) else value
        _UpperCAmelCase        = value







    def   _snake_case       (      self      :							str  ,       *__UpperCamelCase      :							Tuple  ,       **__UpperCamelCase      :							Optional[int]  )		->BatchEncoding:






        '''simple docstring'''






        _UpperCAmelCase        = kwargs.get("""is_split_into_words"""  ,       __UpperCamelCase  )
        assert self.add_prefix_space or not is_split_into_words, (
            f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							List[Any]  ,       *__UpperCamelCase      :							List[Any]  ,       **__UpperCamelCase      :							int  )		->BatchEncoding:






        '''simple docstring'''






        _UpperCAmelCase        = kwargs.get("""is_split_into_words"""  ,       __UpperCamelCase  )

        assert self.add_prefix_space or not is_split_into_words, (
            f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Any  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Optional[str] = None  )		->Tuple[str]:






        '''simple docstring'''






        _UpperCAmelCase        = self._tokenizer.model.save(__UpperCamelCase  ,       name=__UpperCamelCase  )
        return tuple(__UpperCamelCase  )







    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Optional[Any]=None  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
            return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







    def   _snake_case       (      self      :							Any  ,       __UpperCamelCase      :							List[int]  ,       __UpperCamelCase      :							Optional[List[int]] = None  )		->List[int]:






        '''simple docstring'''






        _UpperCAmelCase        = [self.sep_token_id]
        _UpperCAmelCase        = [self.cls_token_id]

        if token_ids_a is None:
            return len(cls + token_ids_a + sep  ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]

"""simple docstring"""



import math
import unittest


def 			_UpperCamelCase						(    _A   )     ->	bool:





    """simple docstring"""

    assert isinstance(_A       ,   _A   ) and (
        number >= 0
    ), "'number' must been an int and positive"

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5       ,   int(math.sqrt(_A   ) + 1   )       ,   6   ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True




class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							int  )		->Optional[int]:






        '''simple docstring'''






        self.assertTrue(is_prime(2  )  )
        self.assertTrue(is_prime(3  )  )
        self.assertTrue(is_prime(5  )  )
        self.assertTrue(is_prime(7  )  )
        self.assertTrue(is_prime(11  )  )
        self.assertTrue(is_prime(13  )  )
        self.assertTrue(is_prime(17  )  )
        self.assertTrue(is_prime(19  )  )
        self.assertTrue(is_prime(23  )  )
        self.assertTrue(is_prime(29  )  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        with self.assertRaises(__UpperCamelCase  ):
            is_prime(-19  )
        self.assertFalse(
            is_prime(0  )  ,       """Zero doesn't have any positive factors, primes must have exactly two."""  ,       )
        self.assertFalse(
            is_prime(1  )  ,       """One only has 1 positive factor, primes must have exactly two."""  ,       )
        self.assertFalse(is_prime(2 * 2  )  )
        self.assertFalse(is_prime(2 * 3  )  )
        self.assertFalse(is_prime(3 * 3  )  )
        self.assertFalse(is_prime(3 * 5  )  )
        self.assertFalse(is_prime(3 * 5 * 7  )  )


if __name__ == "__main__":
 unittest.main()